Numerical Investigation of Forced Convection Conjugate Heat Transfer from Offset Square Cylinders Placed in a Confined Channel Covered by Solid Wall

Flow over two isothermal offset square cylinders in a confined channel is simulated for different Reynolds numbers to disclose the forced convection heat transfer from the heated square cylinders to the ambient fluid. The spacing between the cylinder in the normal direction and the blockage ratio are fixed. The channel walls are covered by solid walls of thickness equal to the size of the cylinder and conjugate heat transfer is considered by including these walls. Heat transfer from the cylinders to the ambient fluid as well as that conducted within the solid wall through the conjugate interface boundary are investigated in connection with Reynolds number and are reported for both steady and periodic flows. Simulation is carried out for Reynolds number varying from 10 to 100 with air as the fluid. The onset of the vortex begins when the Reynolds number equals 48. The conjugate interface temperature declines when the Reynolds number grows. The isotherms in the solid wall show two dimensionality near the cylinder region.     

Novel Collision Detection and Avoidance System for Midvehicle Using Offset-Based Curvilinear Motion

In recent days, the manufacture of automotive vehicles is dramatically enhanced worldwide. Most vehicle crashes are due to the drive distraction on the real highway roads and traffic-density. In this proposed method, a novel collision detection and avoidance algorithm are coined for Midvehicle Collision Detection and Avoidance System (MCDAS), addressing two scenarios, namely, (a) A rear-end collision avoidance with host vehicle under no front-end vehicle condition and (b) offset-based curvilinear motion under critical conditions, while, suitable parallel parking manoeuvring also addressed using offset-based curvilinear motion. The Monte Carlo analysis of the proposed MCDAS is demonstrated using the Constant Velocity (CV) manoeuvring strategy and simulated with real-time data using the NGSIM database.

     

Microstructure and mechanical properties of a laser welded low carbon–stainless steel joint

This article reports on an experimental investigation to understand and subsequently control the alloying composition in laser welding of austenitic stainless steel and low carbon steel. An energy dispersive spectrometer was used to analyse the alloying composition, while microscopy and tensile tests were used to study the microstructure, and mechanical performance of the welded joint respectively. The investigation revealed that above a certain specific point energy the material within the melt pool is well mixed and the laser beam position can be used to control the mechanical properties of the joint. This finding was confirmed using a numerical model based on computational fluid dynamics (CFD) of melt pool dynamics. Strategies to control the composition leading to dramatic changes in hardness, microstructure and mechanical properties of the dissimilar laser welded joint are discussed.     

Prediction of the tensile capacity of cold formed angles experiencing shear lag

The present study deals with the shear lag phenomenon in cold formed angles under tension, which are connected on one leg. A new expression for shear lag factor which represents the net section reduction coefficient has been suggested in the present paper. The proposed expression based on the regression analysis of 108 experimental results reported in the literature is validated by experiments involving net section failure in angles under tension. Totally 18 experiments were carried out on single angles fastened with bolts to the gusset plates under tension loaded upto net section rupture mode of failure. The experimental test parameters considered are number of bolts, pitch and shear lag distances and ratio of connected leg length to unconnected leg length. The tensile capacities are evaluated by various specifications such as AS/NZS:4600:2005, NAS:2001, AISC:2005, BS:5950-Part5:1998, IS:800-2007 and the proposed equation. A comparative study of tensile capacities predicted based on various codes and the experiment results is presented in this paper. For the tested range of specimens, both NAS:2001 and AISC:2005 standards over-predicted the capacities for all the specimens. The IS:800-2007 and AS/NZS 4600:2005 predictions are good for the specimens with three bolted connections and unconservative in the case of specimen with two bolts. Both BS:5950-Part-5:1998 and the proposed equation for IS:801 predict good estimate of the tensile capacity of cold formed angle members. The proposed equation for cold formed steel tension members, which is in the same format of IS:800 (2007) (Indian code for Hot rolled steel design), has been demonstrated to be good.     

Threshold accepting and Ant-colony optimization algorithms for scheduling m-machine flow shops with lot streaming

This paper addresses the problem of making sequencing and scheduling decisions for n jobs m machines flow shops under lot sizing environment. Lot streaming (lot sizing) is the process of creating sublots to move the completed portion of a production sublots to down stream machines. There is a scope for efficient algorithms for scheduling problems in m-machine flow shop with lot streaming. In recent years, much attention is given to heuristics and search techniques. On this concern this paper proposes Ant-colony optimization algorithm (ACO) and threshold accepting algorithm (TA) to evolve best sequence for makespan/total flow time criterion for m-machine flow shop involved with lot streaming and setup time. The following two algorithms are used to evaluate the performance of the proposed ACO and TA: (i) Baker's algorithm (BA), an optimal solution procedure for two-machine flow shop problem with lot streaming and makespan objective criterion and (ii) genetic and hybrid genetic algorithm for m-machine flow shop problem with lot streaming and makespan and total flow time criteria.     

Streaming analysis in wireless sensor networks

Two new incremental models for online anomaly detection in data streams at nodes in wireless sensor networks are discussed. These models are incremental versions of a model that uses ellipsoids to detect first, second, and higher‐ordered anomalies in arrears. The incremental versions can also be used this way but have additional capabilities offered by processing data incrementally as they arrive in time. Specifically, they can detect anomalies ‘on‐the‐fly’ in near real time. They can also be used to track temporal changes in near real‐time because of sensor drift, cyclic variation, or seasonal changes. One of the new models has a mechanism that enables graceful degradation of inputs in the distant past (fading memory). Three real datasets from single sensors in deployed environmental monitoring networks are used to illustrate various facets of the new models. Examples compare the incremental version with the previous batch and dynamic models and show that the incremental versions can detect various types of dynamic anomalies in near real time. Copyright © 2012 John Wiley & Sons, Ltd.     

A Novel Approach to Improve the Speech Intelligibility Using Fractional Delta-amplitude Modulation Spectrogram

Abstract

Speech enhancement is an interesting research area that aims at improving the quality and intelligibility of the speech that is affected by the additive noises, such as airport noise, train noise, restaurant noise, and so on. The presence of these background noises degrades the comfort of listening of the end user. This article proposes a speech enhancement method that uses a novel feature extraction which removes the noise spectrum from the noisy speech signal using a novel fractional delta-AMS (amplitude modulation spectrogram) feature extraction and the D-matrix feature extraction method. The fractional delta-AMS feature extraction strategy is the modification of the delta-AMS with the fractional calculus that increases the sharpness of the feature extraction. The extracted features from the frames are used to determine the optimal mask of all the frames of the noisy speech signal and the mask is employed for training the deep belief neural networks (DBN). The two metrics root mean square error (RMSE) and perceptual evaluation of speech quality (PESQ) are used to evaluate the method. The proposed method yields a better value of PESQ at all level of noise and RMSE decreases with increased noise level.     

Ellipsoidal neighbourhood outlier factor for distributed anomaly detection in resource constrained networks

Anomaly detection in resource constrained wireless networks is an important challenge for tasks such as intrusion detection, quality assurance and event monitoring applications. The challenge is to detect these interesting events or anomalies in a timely manner, while minimising energy consumption in the network. We propose a distributed anomaly detection architecture, which uses multiple hyperellipsoidal clusters to model the data at each sensor node, and identify global and local anomalies in the network. In particular, a novel anomaly scoring method is proposed to provide a score for each hyperellipsoidal model, based on how remote the ellipsoid is relative to their neighbours. We demonstrate using several synthetic and real datasets that our proposed scheme achieves a higher detection performance with a significant reduction in communication overhead in the network compared to centralised and existing schemes.